1. Technical Field
The present invention relates to visualization and computer aided diagnosis and detection of pulmonary embolism, and more particularly, to a system and method for tree projection for detection of pulmonary embolism.
2. Discussion of the Related Art
A pulmonary embolism (PE) occurs when a piece of a blood clot from a deep vein thrombosis (DVT) breaks off and travels to an artery in a lung where it blocks the artery, damages the lung and puts a strain on the heart. This short-term complication is potentially life threatening and occurs in about ten percent of patients with acute DVT events. It may be even more common than generally realized because the majority of embolisms occur without symptoms.
Although PE is one of the most common causes of unexpected death in the United States, it may also be one of the most preventable. Prompt treatment with anticoagulants is essential to prevent loss of life. However, such treatment carries risks, making correct diagnosis critical. As a result, computed tomography angiography (CTA) is gaining increasing acceptance as a method of diagnosis by offering sensitivity and specificity comparable or superior to alternative methods such as pulmonary angiography and ventilation-perfusion scans.
Images acquired from 16-slice computed tomography (CT) scanners used during CTA provide very high-resolution data allowing for enhanced detection of emboli located in sub-segmental arteries. Analysis of the high-resolution data via two-dimensional (2D) slices involves tracking individual vessels and examining their contents. This analysis, however, can be time consuming, especially for peripheral arteries. For example, a radiologist must navigate through individual 2D slices while at the same time remembering the locations of the vessels being tracked. However, because the radiologist can only track a limited number of vessels at one time, the entire tracking process must be repeated.
Since intravenous contrast material does not penetrate into clots, radiologists identify pulmonary emboli on the 2D slices by looking within arteries for dark areas surrounded by bright contrast-filled blood. In a previous method for three-dimensional (3D) visualization of PE, a shaded surface display (SSD) of a pulmonary vessel tree is created and values inside the vessels are used to color their surface. The resulting visualization shows unblocked vessels as bright white areas and potential clots as dark spots. This 3D visualization method simplifies the search for peripheral PE because the entire vessel tree is shown at once and vessel tracking is not necessary.
The above-mentioned visualization also shows emboli in their anatomic context. However, the complete pulmonary vessel tree can be quite complex with numerous branching vessels. An exemplary vessel tree is shown in image (a) of FIG. 1 and an SSD for PE visualization is shown in image (b) of FIG. 1. As shown in FIG. 1, while the vessels on the front side of the trees may be readily inspected for suspicious dark areas, vessels on the far side of the trees are obscured by nearer vessels. Thus, in order to check for PE throughout a vessel tree, the user must navigate around the vessel tree taking care to inspect all sides.
In another method for PE detection, a paddlewheel of maximum intensity projections (MIPs) is rendered about the heart. This method does not require a radiologist to repetitively inspect the vessel tree when examining different regions. However, the vessels must be tracked and the entire dataset rendered without regard to content. Thus, regions of the parenchyma, airways and other extraneous structures influence the renderings. As a result, this prevents the use of an average intensity projection or other types of projection methods because the extraneous structures interfere with the visualization of the periphery arteries.
The paddlewheel method further requires thin slabs to achieve effective sensitivity because the MIPs may obscure PE regions if there is a partial blockage or a bright region in an area. This results in a significant number of images that must be analyzed to achieve an acceptable sensitivity. Accordingly, there is a need for an effective PE detection technique that allows for the examination of 2D images without requiring numerous thin MIP slabs.